Frequency-aware multimodal fusion framework for emotion recognition in conversation
FAMF consistently improves robust recognition of high-arousal and incongruent emotions, achieving 73.05 weighted-F1 on IEMOCAP and 66.75 weighted-F1 on MELD.
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FAMF consistently improves robust recognition of high-arousal and incongruent emotions, achieving 73.05 weighted-F1 on IEMOCAP and 66.75 weighted-F1 on MELD.
A dynamic re-initialization framework for EEG–fNIRS multimodal decoding achieves cross-modal balanced learning through a diagnosis–adjustment–re-initialization mechanism, which achieves cross-modal balanced learning through a diagnosis–adjustment–re-initialization mechanism.
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